

In [2]:

    
%matplotlib inline
from __future__ import (absolute_import, unicode_literals, division,
                        print_function)
from srttools.core.calibration import *
import numpy as np
import os

Preliminaries

TBW

Rapid calibration: Total power

In the following, I will assume that all fits went perfectly. This is not the case, calibrators are not all very reliable, etc. But still, it is good for first-order calibration.



In [3]:

    
# read config file
config_file_tp = \
    os.path.abspath(os.path.join('/Users/meo/devel/spyder_projects/srt-single-dish-tools',
                                     'TEST_DATASET',
                                     'test_calib.ini'))

Get the full table of scans, each fit with a linear + Gaussian function. If a source is in the calibrator list, the conversion between flux and counts is done automatically.



In [4]:

    
full_table_tp = get_full_table(config_file_tp, channel='Ch0')









    



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../../TEST_DATASET/20150624/tp/20150624-141856-scicom-3C286/summary.fits is an invalid file
Loading file ../../TEST_DATASET/20150624/tp/20150624-142130-scicom-3C295/20150624-142130-scicom-3C295_005_003.fits
Loading file ../../TEST_DATASET/20150624/tp/20150624-142130-scicom-3C295/20150624-142148-scicom-3C295_005_004.fits
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../../TEST_DATASET/20150624/tp/20150624-142130-scicom-3C295/summary.fits is an invalid file
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Loading file ../../TEST_DATASET/20150624/tp/20150624-144220-scicom-3C279/20150624-144311-scicom-3C279_010_006.fits
Loading file ../../TEST_DATASET/20150624/tp/20150624-144220-scicom-3C279/summary.fits
../../TEST_DATASET/20150624/tp/20150624-144220-scicom-3C279/summary.fits is an invalid file

Here's the table as it is now. Notice the Flux = $1\pm0$ in Sources, and the correct values for calibrators



In [5]:

    
full_table_tp









    Out[5]:




<Table length=119>

Dir File Scan Type Source Time Frequency Bandwidth Counts Counts Err Width Flux Density Flux Density Err Kind Elevation Flux/Counts Flux/Counts Err RA Dec Fit RA Fit Dec
str200 str200 str200 str200 float128 float64 float64 float64 float64 float64 float64 float64 str200 float64 float64 float64 float64 float64 float64 float64
20150624/tp/20150624-130110-scicom-3C48 20150624-130110-scicom-3C48_001_003.fits Dec> 3C48 57197.542526 6900.0 680.0 48.4871056694 0.161149578883 0.0182190072702 3.85602836538 0.0 Calibrator 18.8371611224 54.0782802408 0.179732156977 24.42207125 33.159755 nan 33.1610901969
20150624/tp/20150624-130110-scicom-3C48 20150624-130127-scicom-3C48_001_004.fits Dec< 3C48 57197.5427251 6900.0 680.0 48.0665170427 0.129643775841 0.0185177497949 3.85602836538 0.0 Calibrator 18.7980871342 54.5514726213 0.147134831551 24.42207125 33.159755 nan 33.160499193
20150624/tp/20150624-130110-scicom-3C48 20150624-130147-scicom-3C48_001_005.fits RA> 3C48 57197.5429462 6900.0 680.0 51.0601225897 0.123135376588 0.018222305147 3.85602836538 0.0 Calibrator 18.7171572129 51.3531726026 0.123842089025 24.42207125 33.159755 24.4146467081 nan
20150624/tp/20150624-130110-scicom-3C48 20150624-130204-scicom-3C48_001_006.fits RA< 3C48 57197.543151 6900.0 680.0 50.7214386717 0.138853789244 0.018112934866 3.85602836538 0.0 Calibrator 18.6862315425 51.6960748181 0.141521929692 24.42207125 33.159755 24.4153187009 nan
20150624/tp/20150624-130416-scicom-3C123 20150624-130416-scicom-3C123_002_003.fits Dec> 3C123 57197.5446707 6900.0 680.0 152.069711532 0.34613336983 0.0183468783824 11.3335366754 0.0 Calibrator 49.6292219099 50.6794210475 0.115353929534 69.2682304167 29.6705052778 nan 29.6728766371
20150624/tp/20150624-130416-scicom-3C123 20150624-130433-scicom-3C123_002_004.fits Dec< 3C123 57197.5448693 6900.0 680.0 150.574125663 0.303929511589 0.0186059842283 11.3335366754 0.0 Calibrator 49.5984874295 51.1827972128 0.103310993772 69.2682304167 29.6705052778 nan 29.6733246142
20150624/tp/20150624-130416-scicom-3C123 20150624-130452-scicom-3C123_002_005.fits RA> 3C123 57197.5450968 6900.0 680.0 151.720304614 0.272409842295 0.0184031674636 11.3335366754 0.0 Calibrator 49.5135436244 50.7961341027 0.0912031314156 69.2682304167 29.6705052778 69.2639462592 nan
20150624/tp/20150624-130416-scicom-3C123 20150624-130509-scicom-3C123_002_006.fits RA< 3C123 57197.5452941 6900.0 680.0 152.663843437 0.298608943885 0.0184155535791 11.3335366754 0.0 Calibrator 49.4759863926 50.4821886163 0.0987426537179 69.2682304167 29.6705052778 69.2644480502 nan
20150624/tp/20150624-133844-scicom-3C279 20150624-133918-scicom-3C279_008_005.fits RA> 3C279 57197.56901 6900.0 680.0 247.879744536 0.926266063458 0.0182321510138 0.00147058823529 0.0 Source 13.0379027778 0.0040342142593 1.5074873375e-05 194.0465275 -5.78931241667 194.049297138 nan
20150624/tp/20150624-133844-scicom-3C279 20150624-133936-scicom-3C279_008_006.fits RA< 3C279 57197.5692079 6900.0 680.0 245.290083071 0.88579628461 0.0182109281478 0.00147058823529 0.0 Source 13.0618930964 0.00407680566405 1.47222393383e-05 194.0465275 -5.78931241667 194.050248401 nan
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
20150624/tp/20150624-143736-scicom-3C279 20150624-143811-scicom-3C279_007_005.fits RA> 3C279 57197.609892 6900.0 680.0 238.956086964 2.72222036948 0.0169250480127 0.00147058823529 0.0 Source 23.3995705881 0.00418486933188 4.76746028259e-05 194.0465275 -5.78931241667 194.046616857 nan
20150624/tp/20150624-143736-scicom-3C279 20150624-143828-scicom-3C279_007_006.fits RA< 3C279 57197.6100958 6900.0 680.0 246.802556627 5.76380338467 0.0173305876947 0.00147058823529 0.0 Source 23.4267289363 0.00405182188413 9.4625862102e-05 194.0465275 -5.78931241667 194.042342887 nan
20150624/tp/20150624-134019-scicom-3C279 20150624-134054-scicom-3C279_009_005.fits RA> 3C279 57197.5701118 6900.0 680.0 244.299070999 1.02429990028 0.018441938908 0.00147058823529 0.0 Source 13.3373357483 0.00409334344134 1.71626165487e-05 194.0465275 -5.78931241667 194.049354201 nan
20150624/tp/20150624-134019-scicom-3C279 20150624-134111-scicom-3C279_009_006.fits RA< 3C279 57197.5703172 6900.0 680.0 248.783964424 1.12546318996 0.018565183089 0.00147058823529 0.0 Source 13.3611259581 0.00401955167133 1.81838787589e-05 194.0465275 -5.78931241667 194.048906803 nan
20150624/tp/20150624-125721-scicom-3C295 20150624-125721-scicom-3C295_005_003.fits Dec> 3C295 57197.5398762 6900.0 680.0 55.1310829748 0.155550965169 0.0182164343783 4.21567344465 0.0 Calibrator 26.3747382322 51.9971273495 0.146708588129 212.835279167 52.2026444444 nan 52.1986497392
20150624/tp/20150624-125721-scicom-3C295 20150624-125739-scicom-3C295_005_004.fits Dec< 3C295 57197.5400758 6900.0 680.0 54.6793294323 0.153936631431 0.0183042852881 4.21567344465 0.0 Calibrator 26.4266094092 52.4267208856 0.147594948473 212.835279167 52.2026444444 nan 52.1993439211
20150624/tp/20150624-125721-scicom-3C295 20150624-125758-scicom-3C295_005_005.fits RA> 3C295 57197.5403038 6900.0 680.0 55.7359402726 0.156948710232 0.0185394449515 4.21567344465 0.0 Calibrator 26.4602512424 51.432844379 0.144831477667 212.835279167 52.2026444444 212.840609887 nan
20150624/tp/20150624-125721-scicom-3C295 20150624-125818-scicom-3C295_005_006.fits RA< 3C295 57197.5405266 6900.0 680.0 56.3782164312 0.163235308894 0.0185827224963 4.21567344465 0.0 Calibrator 26.485028968 50.8469072599 0.147220170099 212.835279167 52.2026444444 212.841710987 nan
20150624/tp/20150624-140509-scicom-3C279 20150624-140544-scicom-3C279_009_005.fits RA> 3C279 57197.5873561 6900.0 680.0 265.431755753 2.33964758558 0.018292857964 0.00147058823529 0.0 Source 17.7985927693 0.00376744672906 3.32081503151e-05 194.0465275 -5.78931241667 194.054567429 nan
20150624/tp/20150624-140509-scicom-3C279 20150624-140601-scicom-3C279_009_006.fits RA< 3C279 57197.5875541 6900.0 680.0 282.134140147 3.00594283813 0.0185885964101 0.00147058823529 0.0 Source 17.8123773952 0.00354441330453 3.77632560971e-05 194.0465275 -5.78931241667 194.050281555 nan

To select the calibrator and the source data from the table, it is sufficient to do



In [6]:

    
calibrator_table_tp = full_table_tp[full_table_tp["Kind"] == "Calibrator"]

# Similarly for the source table
source_table_tp = full_table_tp[full_table_tp["Kind"] == "Source"]

To obtain the mean calibration values and their errors (Jansky/Counts), we take the Flux / Counts column and normalize by the bandwidth



In [7]:

    
fc = np.mean(calibrator_table_tp["Flux/Counts"]) / calibrator_table_tp["Bandwidth"][0]
fce = np.sqrt(np.sum(calibrator_table_tp["Flux/Counts Err"] ** 2))\
    / len(calibrator_table_tp) / calibrator_table_tp["Bandwidth"][0]

Then, the calibrated source flux can be obtained through



In [8]:

    
source_table_tp["Flux"] = source_table_tp["Counts"] * fc
source_table_tp["Flux Err"] = \
    (source_table_tp["Counts Err"] / source_table_tp["Counts"] + fce / fc) * \
    source_table_tp["Flux"]

Let's look at 3C279 data



In [9]:

    
filtered = source_table_tp[source_table_tp["Source"] == "3C279"]
print(filtered[("Source", "Counts", "Counts Err", "Flux", "Flux Err")])









    



Source     Counts      Counts Err        Flux         Flux Err   
------ ------------- -------------- ------------- ---------------
 3C279 247.879744536 0.926266063458 18.6222301021 0.0750688951558
 3C279 245.290083071  0.88579628461 18.4276790234 0.0719712862789
 3C279 263.493085646 0.932953180594 19.7951990002 0.0759165804616
 3C279 252.546476397 0.991022959482 18.9728233089 0.0800370366068
 3C279 259.224171234  2.19641005498 19.4744922535  0.170740712906
 3C279 249.035128039 0.902199869335 18.7090295197 0.0732864494089
 3C279 247.478423703 0.880833541398 18.5920804466 0.0716468527846
 3C279 252.503222998  3.74125914853 18.9695738512  0.286650506057
 3C279 296.181490346  3.45885085165 22.2509502563  0.266400280973
 3C279 299.272014527  5.44834682753 22.4831291805  0.415931638193
   ...           ...            ...           ...             ...
 3C279 348.915038069  9.92934077366 26.2126142544  0.753669003459
 3C279 264.041857676  6.51269296828 19.8364260841  0.495112618007
 3C279 273.487366926  2.96035532639 20.5460300375  0.228448364306
 3C279  258.35484649   2.7410600649 19.4091833052  0.211638899418
 3C279 247.587364445  22.3522667125  18.600264736   1.68471357235
 3C279 238.956086964  2.72222036948 17.9518308124  0.209794519464
 3C279 246.802556627  5.76380338467 18.5413052119  0.438470220928
 3C279 244.299070999  1.02429990028 18.3532281849 0.0823546006137
 3C279 248.783964424  1.12546318996 18.6901605853 0.0900537896221
 3C279 265.431755753  2.33964758558  19.940843659  0.181638873585
 3C279 282.134140147  3.00594283813 21.1956280951  0.232064408899
Length = 25 rows

If we want to play it fancy, we can obtain a histogram of the calibrated fluxes, finding possible outliers.



In [10]:

    
from astropy.visualization import hist
_ = hist(filtered["Flux"], bins='knuth')
plt.xlabel("Flux")
plt.ylabel("Occurrences in fit")









    Out[10]:





<matplotlib.text.Text at 0x10f310eb8>

The function show_calibration plots this and the calibrator data.



In [11]:

    
show_calibration(full_table_tp)









    



Source  Flux Density  Flux Density Err     Counts      Counts Err  
------ ------------- ----------------- ------------- --------------
 3C279 18.6222301021 0.000110395434053 247.879744536 0.926266063458
 3C279 18.4276790234 0.000105840126881 245.290083071  0.88579628461
 3C279 19.7951990002 0.000111642030091 263.493085646 0.932953180594
 3C279 18.9728233089 0.000117701524422 252.546476397 0.991022959482
 3C279 19.4744922535 0.000251089283686 259.224171234  2.19641005498
 3C279 18.7090295197 0.000107774190307 249.035128039 0.902199869335
 3C279 18.5920804466 0.000105363018801 247.478423703 0.880833541398
 3C279 18.9695738512 0.000421544861849 252.503222998  3.74125914853
 3C279 22.2509502563 0.000391765119079 296.181490346  3.45885085165
 3C279 22.4831291805 0.000611664173813 299.272014527  5.44834682753
   ...           ...               ...           ...            ...
 3C279 26.2126142544  0.00110833676979 348.915038069  9.92934077366
 3C279 19.8364260841 0.000728106791187 264.041857676  6.51269296828
 3C279 20.5460300375  0.00033595347692 273.487366926  2.96035532639
 3C279 19.4091833052 0.000311233675614  258.35484649   2.7410600649
 3C279  18.600264736  0.00247751995934 247.587364445  22.3522667125
 3C279 17.9518308124 0.000308521352153 238.956086964  2.72222036948
 3C279 18.5413052119 0.000644809148423 246.802556627  5.76380338467
 3C279 18.3532281849 0.000121109706785 244.299070999  1.02429990028
 3C279 18.6901605853 0.000132432043562 248.783964424  1.12546318996
 3C279  19.940843659 0.000267115990566 265.431755753  2.33964758558
 3C279 21.1956280951 0.000341271189558 282.134140147  3.00594283813
Length = 25 rows

There seems to be a strong dependence on elevation. Let's see if results with Channel 1 are consistent.



In [12]:

    
%%capture shutup
full_table_tp1 = get_full_table(config_file_tp, channel='Ch1')

# ----- This time I captured the output of the function above, full of "loading file..." 
# and "saving to file...", not very informative



In [13]:

    
show_calibration(full_table_tp1)









    



Source  Flux Density  Flux Density Err     Counts      Counts Err  
------ ------------- ----------------- ------------- --------------
 3C279 20.7231098149 0.000188240806048 321.799080503  1.87524522081
 3C279 20.5520722752 0.000149847306892 319.143121842  1.47076154082
 3C279 18.2337816143 0.000211102944817 283.143515138   2.1301648177
 3C279 18.5084480628  0.00024133654133 287.408676657  2.44792252695
 3C279 19.5971923937 8.97967283216e-05 304.315257172 0.841846187164
 3C279 19.1461961191 7.93751242246e-05 297.311955652 0.734247937509
 3C279 19.6857016008 0.000100199436019 305.689673546 0.951212090512
 3C279 20.9326122508 9.28979580887e-05 325.052341806 0.867346062183
 3C279 18.5354887066 0.000134716294427 287.828577647  1.32193113309
 3C279 18.3125392639  0.00012455217717 284.366504324  1.21581419532
   ...           ...               ...           ...            ...
 3C279   20.38081931 0.000111299162172 316.483817941  1.06464606727
 3C279 20.3606343222 0.000200672279254 316.170375097  2.00848110569
 3C279 20.7676226702 0.000124049444211 322.490298955   1.1971821026
 3C279 20.9687510188 0.000120098880541 325.613523135  1.15437505729
 3C279 20.9000739553 0.000134245378887 324.547070459  1.30412613909
 3C279 19.9223352169 9.74354453292e-05 309.364241734 0.920741846457
 3C279 19.7320049547 8.28008298449e-05 306.408695781 0.767242157874
 3C279 19.9362008998 0.000154170671546 309.579555172   1.5197559137
 3C279 15.5441969795  0.00249476050854 241.378265127  26.2587891952
 3C279 20.5826148172  0.00348486413921 319.617402101  36.6863353466
 3C279 20.6824688698 0.000110670673834 321.167987058  1.05637258666
Length = 30 rows

Results for Channel 1 are largely consistent. Encouraging!

And the Roach 2 data?



In [14]:

    
config_file_r2 = \
    os.path.abspath(os.path.join('/Users/meo/devel/spyder_projects/srt-single-dish-tools',
                                     'TEST_DATASET',
                                     'test_calib_roach.ini'))



In [15]:

    
%%capture shutup
full_table_r2 = get_full_table(config_file_r2, channel='Ch0')



In [16]:

    
show_calibration(full_table_r2)









    



Source  Flux Density  Flux Density Err     Counts      Counts Err 
------ ------------- ----------------- ------------- -------------
 3C279 14.5492624827 0.000185866828017 4810277.58853 89304.3263179
 3C279 15.2065355481 0.000267558627306 5027585.22869 129687.971419
 3C279 14.9329623059 0.000171649979209 4937136.43533 82178.0058673
 3C279 17.5900104045 0.000142481674472 5815609.75558 67187.9575418
 3C279 16.6173833944 0.000140305973898 5494039.78499 66300.9985056
 3C279 15.9057806362 8.13149766784e-05  5258769.6602 37186.0900709
 3C279 14.9913380036 6.28529851028e-05 4956436.61022 28210.7820524
 3C279 14.1868256284  6.0650158976e-05 4690448.70513 27277.1779532
 3C279 14.2087277608 5.69309167036e-05 4697689.98878 25428.3695952
 3C279 17.5218431775 0.000238320627303   5793072.305 114730.832625
   ...           ...               ...           ...           ...
 3C279 14.8190413096 0.000217648126903 4899471.87219 105012.361927
 3C279 15.6489935001 0.000110915499946 5173870.68975 51916.5791317
 3C279 13.5313937308  0.00075601669949 4473749.79194 372259.745958
 3C279 14.8725834609 0.000270892967913 4917173.97982 131407.506866
 3C279 15.3761643182 0.000251807825303 5083667.89763  121843.18385
 3C279 17.7712770436 0.000196147364373  5875540.1371 93766.5960659
 3C279 15.4069219525 0.000290040010368 5093836.98757 140797.600259
 3C279 13.6266070211 0.000645772457084 4505229.21277 317567.514987
 3C279 15.7831197072 0.000114499459972 5218215.50026 53667.4906004
 3C279 16.7141192294 0.000168945740516 5526022.58958 80485.2196238
 3C279 14.6220207948 0.000550835387416 4834332.94381  270288.83457
Length = 28 rows



In [17]:

    
%%capture shutup
full_table_r21 = get_full_table(config_file_r2, channel='Ch1')



In [18]:

    
show_calibration(full_table_r21)









    



Source  Flux Density  Flux Density Err     Counts      Counts Err 
------ ------------- ----------------- ------------- -------------
 3C279 17.3602293783  4.7460927577e-05 4709279.25118 16420.7125154
 3C279 17.3591214735 8.57832511955e-05 4708978.71175  32014.342216
 3C279 16.8274364717 5.48792731853e-05 4564749.44539 19527.9888779
 3C279 17.8136978161 5.36072744804e-05 4832290.84615 18846.1530581
 3C279 17.9863753466  5.5876789692e-05 4879132.77975 19740.8657181
 3C279 17.9414861032 6.72419286632e-05 4866955.75271 24372.8440232
 3C279 16.6660087495  5.1512621155e-05 4520959.22775 18184.9750261
 3C279 16.3107193591 4.59106856357e-05 4424580.49233  15964.705588
 3C279 14.8362789799 8.38440728444e-05 4024611.61325  31645.451703
 3C279 15.0126656907 9.51207940226e-05 4072459.72972 36204.6002809
   ...           ...               ...           ...           ...
 3C279 17.9143788037 6.57009662464e-05 4859602.40269 23750.3373027
 3C279 17.6562821447 9.68325828729e-05 4789588.97058 36460.8512107
 3C279 18.0833033281 6.77102209934e-05 4905426.26481 24539.7743997
 3C279 16.9501115648 5.49284941721e-05 4598027.30469 19527.5861406
 3C279 17.5496046792 5.71680952326e-05 4760650.75992 20339.0428295
 3C279 17.1682892531 6.27967120742e-05   4657211.987 22692.8465173
 3C279 17.8419220408 5.78878645608e-05 4839947.18253 20583.2348129
 3C279 17.7491293444 6.19658929906e-05 4814775.46907 22258.0484623
 3C279 17.8087919679  5.2292662284e-05 4830960.04525 18312.0512521
 3C279  17.783454174 0.000134936971785 4824086.70593  51944.438427
 3C279 17.4954839285 9.89700597406e-05 4745969.51792  37357.375738
Length = 31 rows

Notice that for Roach 2 data the results are largely consistent. But:

Error bars of calibrators are lower
Flux values for the source are lower
Conversion factors for calibrators show a strong dependence on elevation and / or flux

These things are worth further investigation.

Behavior of single calibrators

Let's see where this apparent discrepancy can come from, by plotting all curves that correspond to a given calibrator

Total power



In [19]:

    
_ = get_full_table(config_file_tp, channel='Ch0', plotall=True, verbose=False)
plt.show()









    



../../TEST_DATASET/20150624/tp/20150624-124434-scicom-3C48/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-124810-scicom-3C123/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-125057-scicom-3C147/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-125455-scicom-3C286/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-125721-scicom-3C295/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-130110-scicom-3C48/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-130416-scicom-3C123/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-130703-scicom-3C147/summary.fits is an invalid file
fit failed
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../../TEST_DATASET/20150624/tp/20150624-131333-scicom-3C295/summary.fits is an invalid file
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fit failed
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../../TEST_DATASET/20150624/tp/20150624-134641-scicom-3C48/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-134944-scicom-3C123/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-135222-scicom-3C147/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-135633-scicom-3C286/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-135906-scicom-3C295/summary.fits is an invalid file
fit failed
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../../TEST_DATASET/20150624/tp/20150624-140333-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-140644-scicom-3C279/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-141202-scicom-3C123/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-141441-scicom-3C147/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-141856-scicom-3C286/summary.fits is an invalid file
../../TEST_DATASET/20150624/tp/20150624-142130-scicom-3C295/summary.fits is an invalid file
fit failed
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../../TEST_DATASET/20150624/tp/20150624-142858-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-143034-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-143208-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-143343-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-143911-scicom-3C279/summary.fits is an invalid file
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../../TEST_DATASET/20150624/tp/20150624-144220-scicom-3C279/summary.fits is an invalid file

In some scans, there is an apparent movement of the Gaussian maximum. This might be due to slight pointing errors, but there is no strong evidence (from the Pointing error vs Elevation plots abeve) that this might produce the steep dependence of calibration on elevation. But there is also a slight change of the Gaussian intensity in these curves. This might be related to elevation. Let's look at the Roach data

Roach 2



In [20]:

    
_ = get_full_table(config_file_r2, channel='Ch0', plotall=True, verbose=False)
plt.show()









    



fit failed
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Here, the change of the Gaussian amplitude is more extreme than in the total power curves. In some sources, like 3C48, the change of intensity is very large. Lower amplitudes at lower elevation, and this might indeed be related to a change of opacity.



In [21]:

    
_ = get_full_table(config_file_r2, channel='Ch1', plotall=True, verbose=False)
plt.show()









    



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In [ ]:

Dir	File	Scan Type	Source	Time	Frequency	Bandwidth	Counts	Counts Err	Width	Flux Density	Flux Density Err	Kind	Elevation	Flux/Counts	Flux/Counts Err	RA	Dec	Fit RA	Fit Dec
str200	str200	str200	str200	float128	float64	float64	float64	float64	float64	float64	float64	str200	float64	float64	float64	float64	float64	float64	float64
20150624/tp/20150624-130110-scicom-3C48	20150624-130110-scicom-3C48_001_003.fits	Dec>	3C48	57197.542526	6900.0	680.0	48.4871056694	0.161149578883	0.0182190072702	3.85602836538	0.0	Calibrator	18.8371611224	54.0782802408	0.179732156977	24.42207125	33.159755	nan	33.1610901969
20150624/tp/20150624-130110-scicom-3C48	20150624-130127-scicom-3C48_001_004.fits	Dec<	3C48	57197.5427251	6900.0	680.0	48.0665170427	0.129643775841	0.0185177497949	3.85602836538	0.0	Calibrator	18.7980871342	54.5514726213	0.147134831551	24.42207125	33.159755	nan	33.160499193
20150624/tp/20150624-130110-scicom-3C48	20150624-130147-scicom-3C48_001_005.fits	RA>	3C48	57197.5429462	6900.0	680.0	51.0601225897	0.123135376588	0.018222305147	3.85602836538	0.0	Calibrator	18.7171572129	51.3531726026	0.123842089025	24.42207125	33.159755	24.4146467081	nan
20150624/tp/20150624-130110-scicom-3C48	20150624-130204-scicom-3C48_001_006.fits	RA<	3C48	57197.543151	6900.0	680.0	50.7214386717	0.138853789244	0.018112934866	3.85602836538	0.0	Calibrator	18.6862315425	51.6960748181	0.141521929692	24.42207125	33.159755	24.4153187009	nan
20150624/tp/20150624-130416-scicom-3C123	20150624-130416-scicom-3C123_002_003.fits	Dec>	3C123	57197.5446707	6900.0	680.0	152.069711532	0.34613336983	0.0183468783824	11.3335366754	0.0	Calibrator	49.6292219099	50.6794210475	0.115353929534	69.2682304167	29.6705052778	nan	29.6728766371
20150624/tp/20150624-130416-scicom-3C123	20150624-130433-scicom-3C123_002_004.fits	Dec<	3C123	57197.5448693	6900.0	680.0	150.574125663	0.303929511589	0.0186059842283	11.3335366754	0.0	Calibrator	49.5984874295	51.1827972128	0.103310993772	69.2682304167	29.6705052778	nan	29.6733246142
20150624/tp/20150624-130416-scicom-3C123	20150624-130452-scicom-3C123_002_005.fits	RA>	3C123	57197.5450968	6900.0	680.0	151.720304614	0.272409842295	0.0184031674636	11.3335366754	0.0	Calibrator	49.5135436244	50.7961341027	0.0912031314156	69.2682304167	29.6705052778	69.2639462592	nan
20150624/tp/20150624-130416-scicom-3C123	20150624-130509-scicom-3C123_002_006.fits	RA<	3C123	57197.5452941	6900.0	680.0	152.663843437	0.298608943885	0.0184155535791	11.3335366754	0.0	Calibrator	49.4759863926	50.4821886163	0.0987426537179	69.2682304167	29.6705052778	69.2644480502	nan
20150624/tp/20150624-133844-scicom-3C279	20150624-133918-scicom-3C279_008_005.fits	RA>	3C279	57197.56901	6900.0	680.0	247.879744536	0.926266063458	0.0182321510138	0.00147058823529	0.0	Source	13.0379027778	0.0040342142593	1.5074873375e-05	194.0465275	-5.78931241667	194.049297138	nan
20150624/tp/20150624-133844-scicom-3C279	20150624-133936-scicom-3C279_008_006.fits	RA<	3C279	57197.5692079	6900.0	680.0	245.290083071	0.88579628461	0.0182109281478	0.00147058823529	0.0	Source	13.0618930964	0.00407680566405	1.47222393383e-05	194.0465275	-5.78931241667	194.050248401	nan
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
20150624/tp/20150624-143736-scicom-3C279	20150624-143811-scicom-3C279_007_005.fits	RA>	3C279	57197.609892	6900.0	680.0	238.956086964	2.72222036948	0.0169250480127	0.00147058823529	0.0	Source	23.3995705881	0.00418486933188	4.76746028259e-05	194.0465275	-5.78931241667	194.046616857	nan
20150624/tp/20150624-143736-scicom-3C279	20150624-143828-scicom-3C279_007_006.fits	RA<	3C279	57197.6100958	6900.0	680.0	246.802556627	5.76380338467	0.0173305876947	0.00147058823529	0.0	Source	23.4267289363	0.00405182188413	9.4625862102e-05	194.0465275	-5.78931241667	194.042342887	nan
20150624/tp/20150624-134019-scicom-3C279	20150624-134054-scicom-3C279_009_005.fits	RA>	3C279	57197.5701118	6900.0	680.0	244.299070999	1.02429990028	0.018441938908	0.00147058823529	0.0	Source	13.3373357483	0.00409334344134	1.71626165487e-05	194.0465275	-5.78931241667	194.049354201	nan
20150624/tp/20150624-134019-scicom-3C279	20150624-134111-scicom-3C279_009_006.fits	RA<	3C279	57197.5703172	6900.0	680.0	248.783964424	1.12546318996	0.018565183089	0.00147058823529	0.0	Source	13.3611259581	0.00401955167133	1.81838787589e-05	194.0465275	-5.78931241667	194.048906803	nan
20150624/tp/20150624-125721-scicom-3C295	20150624-125721-scicom-3C295_005_003.fits	Dec>	3C295	57197.5398762	6900.0	680.0	55.1310829748	0.155550965169	0.0182164343783	4.21567344465	0.0	Calibrator	26.3747382322	51.9971273495	0.146708588129	212.835279167	52.2026444444	nan	52.1986497392
20150624/tp/20150624-125721-scicom-3C295	20150624-125739-scicom-3C295_005_004.fits	Dec<	3C295	57197.5400758	6900.0	680.0	54.6793294323	0.153936631431	0.0183042852881	4.21567344465	0.0	Calibrator	26.4266094092	52.4267208856	0.147594948473	212.835279167	52.2026444444	nan	52.1993439211
20150624/tp/20150624-125721-scicom-3C295	20150624-125758-scicom-3C295_005_005.fits	RA>	3C295	57197.5403038	6900.0	680.0	55.7359402726	0.156948710232	0.0185394449515	4.21567344465	0.0	Calibrator	26.4602512424	51.432844379	0.144831477667	212.835279167	52.2026444444	212.840609887	nan
20150624/tp/20150624-125721-scicom-3C295	20150624-125818-scicom-3C295_005_006.fits	RA<	3C295	57197.5405266	6900.0	680.0	56.3782164312	0.163235308894	0.0185827224963	4.21567344465	0.0	Calibrator	26.485028968	50.8469072599	0.147220170099	212.835279167	52.2026444444	212.841710987	nan
20150624/tp/20150624-140509-scicom-3C279	20150624-140544-scicom-3C279_009_005.fits	RA>	3C279	57197.5873561	6900.0	680.0	265.431755753	2.33964758558	0.018292857964	0.00147058823529	0.0	Source	17.7985927693	0.00376744672906	3.32081503151e-05	194.0465275	-5.78931241667	194.054567429	nan
20150624/tp/20150624-140509-scicom-3C279	20150624-140601-scicom-3C279_009_006.fits	RA<	3C279	57197.5875541	6900.0	680.0	282.134140147	3.00594283813	0.0185885964101	0.00147058823529	0.0	Source	17.8123773952	0.00354441330453	3.77632560971e-05	194.0465275	-5.78931241667	194.050281555	nan